Ferran Alet
Artificial Intelligence · 2017–2022
A researcher in the Science corpus: 6 papers; trusts 212 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 150 citations into it; #9,161 of 35,345 by trust here, #11,072 by citations
- Computer Vision and Pattern Recognition — 84 citations into it; #7,160 of 21,575 by trust here, #8,296 by citations
- Reinforcement Learning — 43 citations into it
- Model Architectures — 36 citations into it
- Representation & Self-Supervised Learning — 25 citations into it
- Graph Learning — 22 citations into it
- Optimization & Training — 22 citations into it
- Natural Language Processing — 18 citations into it
Whom Ferran Alet trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- J. Urgen Schmidhuber
- Kaiming He
- Alex Krizhevsky
- Yann LeCun
- Chelsea Finn
- Ilya Sutskever
- Alex Graves
Who trusts Ferran Alet overall
- Arslan Chaudhry
- Jorge Armando Mendez Mendez
- Shuhao Cao
- Eric Eaton
- Timothy M. Hospedales
- Zong-Yi Li
- Marc'Aurelio Ranzato
- Louis Kirsch
- Oliver Kroemer
- Swarat Chaudhuri
Papers from Ferran Alet's perspective
Ordered by how strongly the researchers Ferran Alet trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Modular meta-learning (2018) — 133 citations
- Meta-learning curiosity algorithms (2020) — 67 citations
- Graph Element Networks: adaptive, structured computation and memory (2019) — 91 citations
- Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching (2017) — 695 citations
- GraphCast: Learning skillful medium-range global weather forecasting (2022) — 419 citations
- A large-scale benchmark for few-shot program induction and synthesis (2021) — 20 citations